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https://issues.apache.org/jira/browse/ARROW-1890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16280369#comment-16280369
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ASF GitHub Bot commented on ARROW-1890:
---------------------------------------
xhochy closed pull request #1393: ARROW-1890: [Python] Fix mask handling for
Date32 NumPy conversions
URL: https://github.com/apache/arrow/pull/1393
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
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diff --git a/cpp/src/arrow/python/numpy_to_arrow.cc
b/cpp/src/arrow/python/numpy_to_arrow.cc
index 2316a796a..bcaccc734 100644
--- a/cpp/src/arrow/python/numpy_to_arrow.cc
+++ b/cpp/src/arrow/python/numpy_to_arrow.cc
@@ -120,6 +120,7 @@ int64_t MaskToBitmap(PyArrayObject* mask, int64_t length,
uint8_t* bitmap) {
for (int i = 0; i < length; ++i) {
if (mask_values[i]) {
++null_count;
+ BitUtil::ClearBit(bitmap, i);
} else {
BitUtil::SetBit(bitmap, i);
}
diff --git a/python/pyarrow/tests/test_convert_pandas.py
b/python/pyarrow/tests/test_convert_pandas.py
index a2a1d6b69..a43651ed9 100644
--- a/python/pyarrow/tests/test_convert_pandas.py
+++ b/python/pyarrow/tests/test_convert_pandas.py
@@ -572,6 +572,15 @@ def test_date_infer(self):
expected['date'] = pd.to_datetime(df['date'])
tm.assert_frame_equal(result, expected)
+ def test_date_mask(self):
+ arr = np.array([date(2017, 4, 3), date(2017, 4, 4)],
+ dtype='datetime64[D]')
+ mask = [True, False]
+ result = pa.array(arr, mask=np.array(mask))
+ expected = np.array([None, date(2017, 4, 4)], dtype='datetime64[D]')
+ expected = pa.array(expected, from_pandas=True)
+ assert expected.equals(result)
+
def test_date_objects_typed(self):
arr = np.array([
date(2017, 4, 3),
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> [Python] Masking for date32 arrays not working
> ----------------------------------------------
>
> Key: ARROW-1890
> URL: https://issues.apache.org/jira/browse/ARROW-1890
> Project: Apache Arrow
> Issue Type: Bug
> Reporter: Uwe L. Korn
> Assignee: Uwe L. Korn
> Labels: pull-request-available
> Fix For: 0.8.0
>
>
> {code}
> arr = np.array([date(2017, 4, 3), date(2017, 4, 4)], dtype='datetime64[D]')
> mask = [True, False]
> t32 = pa.date32()
> result = pa.array(arr, mask=np.array(mask))
> expected = pa.array(np.array([None, date(2017, 4, 4)], dtype='datetime64[D]'))
> {code}
> The above code produces an array that has no null values.
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